%0 Conference Paper %1 Borchert2016 %A Borchert, Kathrin %A Hirth, Matthias %A Zinner, Thomas %A Mocanu, Decebal Constantin %B QCMan 2016 (QCMan 2016) %C Würzburg, Germany %D 2016 %K itc itc28 %T Correlating QoE and Technical Parameters of an SAP System in an Enterprise Environment %U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Borchert2016.pdf?inline=true %X The impact of waiting times in interactive and web-based services on the Quality of Experience (QoE) has attracted high interest. Most studies aim at understanding the QoE impact of waiting times in specific laboratories or in the user's domestic environment. Enterprise and working environments have been largely ignored. This mostly stems from two factors: i) the high complexity of enterprise systems hindering the exact monitoring of relevant application response times on user granularity and ii) disturbances of the day-to-day business by user studies resulting in less productive employees. This paper approaches these challenges by combining non-intrusive application monitoring of response times and subjective user ratings on the perceived application performance. The correlation between objective measurements and subjective ratings is evaluated using different machine learning approaches. The results imply a high correlation for specific users, but do not allow to deviate a generic model.

@inproceedings{Borchert2016, abstract = {The impact of waiting times in interactive and web-based services on the Quality of Experience (QoE) has attracted high interest. Most studies aim at understanding the QoE impact of waiting times in specific laboratories or in the user's domestic environment. Enterprise and working environments have been largely ignored. This mostly stems from two factors: i) the high complexity of enterprise systems hindering the exact monitoring of relevant application response times on user granularity and ii) disturbances of the day-to-day business by user studies resulting in less productive employees. This paper approaches these challenges by combining non-intrusive application monitoring of response times and subjective user ratings on the perceived application performance. The correlation between objective measurements and subjective ratings is evaluated using different machine learning approaches. The results imply a high correlation for specific users, but do not allow to deviate a generic model.}, added-at = {2016-08-31T16:30:53.000+0200}, address = {Würzburg, Germany}, author = {Borchert, Kathrin and Hirth, Matthias and Zinner, Thomas and Mocanu, Decebal Constantin}, biburl = {https://www.bibsonomy.org/bibtex/22c311b60d5fcba9badaefe9e3e403457/itc}, booktitle = {QCMan 2016 (QCMan 2016)}, days = {12}, interhash = {9f96dbe5d9f57745f7fad2a1dfa1a953}, intrahash = {2c311b60d5fcba9badaefe9e3e403457}, keywords = {itc itc28}, month = {Sept}, timestamp = {2020-05-26T16:53:35.000+0200}, title = {Correlating QoE and Technical Parameters of an SAP System in an Enterprise Environment}, url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Borchert2016.pdf?inline=true}, year = 2016 }